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Time2Vec: Learning a Vector Representation of Time

T0 review · 1 major / 0 minor · reviewed 2026-05-24 · grok-4.3

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Pith's one-line read Time2Vec replaces raw time inputs with a learned vector that improves performance when added to existing models.

desk verdict Time2Vec is a simple, model-agnostic embedding for time that extends positional ideas to irregular or event data, but the abstract leaves the empirical gains unverified. read the letter →

arxiv 1907.05321 v1 pith:K46YEPRK submitted 2019-07-11 cs.LG

Seyed Mehran Kazemi , Rishab Goel , Sepehr Eghbali , Janahan Ramanan , Jaspreet Sahota , Sanjay Thakur , Stella Wu , Cathal Smyth
show 2 more authors
Pascal Poupart Marcus Brubaker
This is my paper · ORCID
classification cs.LG
keywords timerepresentationtemporalembeddingmodel-agnosticeventmodelingneuralnetworksperiodicfeatures
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Time2Vec as a model-agnostic vector representation of time. It is designed to be plugged into many different neural architectures without changing their core structure. The authors demonstrate that swapping standard time features for this representation raises accuracy or other performance metrics on a range of temporal tasks. The approach focuses on capturing both periodic and non-periodic aspects of time in a single trainable vector. Because the method is orthogonal to architecture design, it can be applied to models that already handle synchronous or asynchronous events.

What carries the argument

Time2Vec, a trainable vector representation of time that combines periodic and non-periodic components and can be imported into existing models.

What would settle it

A controlled experiment in which the same set of models and temporal datasets are run once with raw time inputs and once with Time2Vec inputs, and the latter shows no consistent gain or shows loss.

Watch

Extended reading notes

Core claim

The paper claims that replacing the notion of time with its Time2Vec representation improves the performance of the final model on multiple problems and architectures.

Load-bearing premise

A single learned vector form of time can be used across different models and problems to capture temporal information better than standard time features.

Editorial extensions

If this is right

  • Existing sequence or event models can incorporate Time2Vec without redesigning their layers.
  • Performance gains appear on both synchronous and asynchronous event data.
  • The representation works as a drop-in replacement for conventional time encodings.
  • The same vector can be reused across different downstream tasks once learned.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The vector form might allow transfer of temporal knowledge between unrelated domains if the same Time2Vec module is shared.
  • If the periodic components are fixed rather than learned, the method could become fully parameter-free for certain periodicities.
  • The approach suggests testing whether similar vector encodings help in non-neural models such as decision trees or linear regressors on time-stamped data.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The paper introduces Time2Vec, a model-agnostic, learnable vector representation of time intended to be plugged into existing architectures for tasks involving synchronous or asynchronous events. The central claim is that replacing standard notions of time with this representation improves final model performance across a range of models and problems.

Significance. If the empirical results hold with proper validation, Time2Vec could serve as a lightweight, reusable component for incorporating temporal structure in machine learning pipelines, complementing architecture-specific innovations in time-series and event modeling.

major comments (1)
  1. Abstract: the central claim of performance improvement is stated without any experimental details, baselines, datasets, quantitative results, error bars, or implementation specifics, preventing verification of the claim that Time2Vec substitution improves performance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We address the single major comment below.

read point-by-point responses

  1. Referee: Abstract: the central claim of performance improvement is stated without any experimental details, baselines, datasets, quantitative results, error bars, or implementation specifics, preventing verification of the claim that Time2Vec substitution improves performance.

    Authors: We agree that the abstract states the central claim at a high level without supporting experimental specifics. While abstracts are necessarily concise, the current wording does not adequately convey the scope of the evaluation. In the revised manuscript we will expand the abstract to include brief references to the models tested, the range of problems considered, and the nature of the observed improvements. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The paper presents Time2Vec as a learnable, model-agnostic vector embedding for time that is substituted into existing architectures, with performance gains demonstrated empirically across multiple models and tasks. No derivation chain, equations, or load-bearing steps are visible in the provided abstract or description that reduce by construction to fitted parameters, self-definitions, or self-citation chains; the representation is defined independently and validated externally rather than tautologically.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Only abstract available; no free parameters, axioms, or invented entities can be identified from provided text.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Time2Vec: Learning a Vector Representation of Time." pith.science (2026). https://pith.science/paper/K46YEPRK

@misc{pith2026190705321,
  author       = {Pith},
  title        = {Pith review of: Time2Vec: Learning a Vector Representation of Time},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K46YEPRK}},
  note         = {Machine review of arXiv:1907.05321}
}
read the original abstract

Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model.

Figures

Figures reproduced from arXiv: 1907.05321 by the authors.

Figure 1
Figure 1. Comparing LSTM+T and LSTM+Time2Vec on several datasets. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Comparing TLSTM1 and TLSTM3 on Last.FM and CiteULike in terms of Recall@10 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. The models learned for our synthesized dataset explained in Subsection 5.2 before the final [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: (a) Initial vs. (b) learned weights and frequencies for our synthesized dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: An ablation study of several components in Time2Vec. (a) Comparing different activa [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparing LSTM+T and LSTM+Time2Vec on Event-MNIST. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Comparing LSTM+T and LSTM+Time2Vec on Event-MNIST and raw N_TIDIGITS18. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Comparing LSTM+T and LSTM+Time2Vec on SOF. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Comparing LSTM+T and LSTM+Time2Vec on Last.FM. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Comparing LSTM+T and LSTM+Time2Vec on CiteULike. [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: TLSTM1’s performance on Last.FM with and without Time2Vec. [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: TLSTM1’s performance on CiteULike with and without Time2Vec. [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 13
Figure 13. Figure 13: TLSTM3’s performance on Last.FM with and without Time2Vec. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: TLSTM3’s performance on CiteULike with and without Time2Vec. [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Pith tools

Reviewed May 24, 2026 · model on record in the stance chip above.